Abstract: Flexible job shop scheduling problem is a typical scheduling problem in the field of intelligent manufacturing. It is one of the most key links in manufacturing process planning and management. An effective solution method is of great practical significance to improve production efficiency. Based on the classical grey wolf algorithm, an improved grey wolf algorithm is proposed to solve the flexible job shop scheduling problem with the goal of optimizing the makespan. Firstly, the algorithm adopts the weight based coding form to discretize the continuous coding in the classical wolf swarm algorithm. Secondly, the random walk strategy is added in the iterative optimization process to enhance the local search ability, then the tail elimination strategy is added in the population updating process to avoid local optimization, increase the population diversity and reasonably expand the search range of the algorithm. The simulation results on the standard example show that the improved wolf swarm algorithm has obvious improvement in the optimization ability than the classical grey wolf algorithm. Compared with other intelligent optimization algorithms, the algorithm proposed in this paper has better optimization performance in each example.

Key words: flexible job shop scheduling, makespan, grey wolf optimization algorithm, random walk, local search